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BIOL 339 CUNY Borough of Manhattan Community College Data Manipulation Worksheet<\/h2>\n

Sample Answer for BIOL 339 CUNY Borough of Manhattan Community College Data Manipulation Worksheet Included After Question<\/strong><\/h2>\n

Description<\/h3>\n
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has two scientific goals: 1) to learn how to summarize data and 2) to help you gain a better sense of the terrestrial weather variables that impact the behavior and physiology<\/a> of animals. As we pursue these goals, we will add to our knowledge of RStudio, specifically improving how to make figures that look more professional (sources -Chapter 3 of R for Data Science and ggplot book, https:\/\/ggplot2-book.org\/index.html) and how to manipulate data frames so we can make the visualizations that are needed (material in chapter 5 of R for Data Science). This homework requires one figure with a legend (caption) in Part 1 and 2 figures with legends for Part 2. \u00a0Uploading your R code with the document containing the figures is often helpful. Below are more details about the assignment.<\/p>\n

\"BIOL
BIOL 339 CUNY Borough of Manhattan Community College Data Manipulation Worksheet<\/figcaption><\/figure>\n

Part 1. This first part is to give you experience with the dplyr options to filter rows, arrange rows, select columns, create a new column with mutate and summarize subsets of the data frame. \u00a0The book chapter 5 and the code I have uploaded with the homework give examples to try. \u00a0The requirement here is to use the group_by() statement to group data by day, week or month, calculate the mean of one variable and one at least one other statistical variable such as mode, median, max, min, standard deviation and standard error then plot the data. \u00a0If you want to use standard deviation or standard error look at examples using geom_error to make the figures. In addition to including a legend, compare you compare the figure you have make to a standard boxplot and comment about which you prefer and why.<\/p>\n

Stevenson 2023 HW04 Graph interpretations & Data manipulation Comparative Animal Physiology Biology 337-339 Due Monday Feb 13 Midnight \u2013 upload the assignment to Blackboard HW04 has two scientific goals: 1) to learn how to summarize data and 2) to help you gain a better sense of the terrestrial weather variables that impact the behavior and physiology of animals. As we pursue these goals, we will add to our knowledge of RStudio, specifically improving how to make figures that look more professional (sources -Chapter 3 of R for Data Science and ggplot book, https:\/\/ggplot2book.org\/index.html) and how to manipulate data frames so we can make the visualizations that are needed (material in chapter 5 of R for Data Science). This homework requires one figure with a legend (caption) in Part 1 and 2 figures with legends for Part 2. Uploading your R code with the document containing the figures is often helpful. Below are more details about the assignment. Part 1. This first part is to give you experience with the dplyr options to filter rows, arrange rows, select columns, create a new column with mutate and summarize subsets of the data frame. The book chapter 5 and the code I have uploaded with the homework give examples to try. The requirement here is to use the group_by() statement to group data by day, week or month, calculate the mean of one variable and one at least one other statistical variable such as mode, median, max, min, standard deviation and standard error then plot the data. If you want to use standard deviation or standard error look at examples using geom_error to make the figures. In addition to including a legend, compare you compare the figure you have make to a standard boxplot and comment about which you prefer and why. Part 2. The relationships among terrestrial weather variables. Here you need to make two or more professional looking figures (plot + figure legend) that enlighten the relationship among variables. Weather is driven by a series of processes that are now so well understood and modeled that meteorologists can make accurate short- and long-term forecasts. The goal here is much simpler – to make sense of patterns we see in the data from the Plum Island LTER site. We can use our everyday experiences as our guide. To orient yourself, remember there are three prominent time scales of weather change. (Ignoring other drivers for now.) the daily, annual and high and low cycles of weather cycle changes (days to weeks). 1) The daily cycle of solar input driven by from the rotation of the earth gives us the daily cycle of solar energy input 2) The tilt in the earth and its 3651\/4 day long trip around the sun are largely responsible for giving us the annual cycle. The earth\u2019s tilt and path around the solar determine the number of daylight hours and the path of the sun through the sky. It reaches its highest point above the horizon at solar noon each day and its highest point in the year at the summer solstice in June (19-21th each year). These variations are well studied and change in a very predictable way. See simulations ( https:\/\/ccnmtl.github.io\/astro-simulations\/sun-motion-simulator\/ , http:\/\/andrewmarsh.com\/apps\/staging\/sunpath3d.html. 3) The uneven heating of the earth by the sun causes thermal gradients that drive the movement of air masses in the atmosphere (fronts, cyclones, the jet stream, etc.) and water currents in the ocean together causing our weather. These forces bring complex exposures to cold and hot and wet and dry air masses that circulate across the globe. In Massachusetts these changes usually occur over several days or weeks. Despite the complexity patterns of weather caused by the three drivers listed above, experience and common sense teaches there are many known relationships. Here are some you may have thought about, heard or observed before. Winds usually blow from the west. Winds from the west and south bring warmer air and winds from the north bring cooler air. When we get a nor\u2019easter storm strong winds off the ocean bring moisture as rain or snow. Precipitation is associated with clouds and thus low sunshine or radiation. Precipitation is associated with high relative humidity. Relative humidity goes up at night and can lead to the formation of dew if it gets to 100%. The rise of air temperature lags behind the solar radiation. The maximum daily air temperature is usually several hours later than the maximum radiation and the maximum average daily temperatures occur several months after the maximum daily solar input. High pressures are associated with clear skies and sunny weather. Falling pressures are an indication of storms coming. Solar radiation data (Pyranometer readings) correspond closely with Photosynthetically Active Radiation (PAR) readings. Based on the ideas of relationships stated in the previous paragraph or ideas of your own, create a minimum of two figures (more are fine) to test the relationships. Are the relationships true in the data set we are using? Try to make the figures as much like figures in published papers as possible. Successful figures communicate the results clearly and in an aesthetically pleasing manner. Based on ggplot\u2019s grammar of graphics this means making the data visible and using the theme\u2019s functions to communicate your information clearly. From among the research papers you have read for the course, find and study two or three figures that were published in recent years. Study the graphic elements of the figures. (Note the background style, lack of titles, the numerical range of axes, whether or not they use log axes, the units of the axis labels, the location and relative sizes of the tick and axis labels, the number of panels in the figure, etc.). Use these published figures as guides to what you create. For the overall look use themes such as theme_classic(), theme_bw() and theme_prism() (you need the ggprism library installed and loaded for theme_prism() to work). Making labels with exponents, Greek letter and subscripts can be done. I put an example in the code. http:\/\/abacus.bates.edu\/~ganderso\/biology\/resources\/writing\/HTWtablefigs.html All axis parameters such as the number of tick marks, the number, size and font of the tick mark labels and the labels can be specified if the default do not work well. One important suggestion is to use xlab(0 and ylab() to make your x and y labels be to set the background. If you cannot get elements of the figure the way you want, please comment on your homework what you could not do. One element that we have touch on in the first R homework were axis labels. You can specific axis labels using ylab() and xlab() or labs(y= “y axis name”, x = “x axis name”). You can change the font size with a statement such as + theme(axis.text.x = element_text(size = 20)) or with more terms You also need to write a clear figure legend (called a caption sometimes) for each figure with three objectives in mind 1) Describes that relationship between the variables in the plot. Describe in words what the viewer should be able to see if they had not seen the plot. 2) Make sure to include units and numerical values to allow the reader to compare what is in your words with what is in the plot 3) Provide an interpretation of the data for the reader. What is the take home message? Are the patterns in the plot standard or unexpected? Plum Island Ecosystems LTER DATABASE DATASET ID: Metacat Package ID Year Released to Public Distribution URL for file DATASET TITLE: MON-PR-Met15Min2019.01 knb-lter-pie.542.1 2020 http:\/\/ecosystems.mbl.edu\/PIE\/data-archive\/MON\/MON-PR-Met15Min2019.html PIE LTER year 2019, meteorological data, 15 minute intervals, from the PIE LTER Marshview Farm weather station located in Newbury, MA INVESTIGATOR INFORMATION : Investigator 1 First Name Anne Last Name Giblin Address line 1 Ecosystems Center Address line 2 MBL Address line 3 7 MBL St City Woods Hole State MA Zip Code 02543 Country USA OTHERS: Investigator 2 Hap Garritt DATA FILE INFORMATION: Data File URL http:\/\/ecosystems.mbl.edu\/PIE\/data-archive\/MON\/data\/MON-PR-Met15min2019.csv Data File Name MON-PR-Met15min2019 Beginning Date 01-Jan-2019 End Date 31-Dec-2019 Number of Data Records 35040 Other Files to Reference Availability Status Type 1 Quality Control Information Maintenance Description Final data set for Year 2019 Log of Changes: Version 01: January 29, 2020, data and metadata updates to comply with importation to DEIMS7 and LTER Data Portal. Used MarcrosExportEML_HTML (working)pie_excel2007_Sep2019.xlsm 9\/3\/19 5:19 PM for QA\/QC to EML 2.1.0 MON-PR-MFM Met Station RESEARCH LOCATION: Geographic Description PIE meteorological station at MBL, Marshview Farm, Newbury, MA. Location Bounding Box West Bounding Coordinate East Bounding Coordinate North Bounding Coordinate South Bounding Coordinate OR if single point location Latitude 42.756971 Longitude -70.891366 Elevation TAXONOMIC COVERAGE: Taxonomic Protocols Organisms studied Site 2 Example: Spartina; Spartina patens; Carex aquatilis var. aquatilis; Carex KEYWORD INFORMATION KEYWORDS: PIE LTER, primary production, Massachusetts, Newbury, climate, meteorology, temperature, precipitation, rain, humidity, solar, radiation, P KeywordThesaurus ABSTRACT: Year 2019 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, pho active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and meas reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric 15 minute totals are reported for precipitation. METHODS: The data file is a summary of daily weather data from the PIE LTER weather station located at MBL Marshview Farm in Newbury, The weather station is solar powered using a Solarex MSX-20 20 watt solar module including a 12V charger and regulator and 12 AH battery. The system was supplied by Campbell Scientific Inc., The data logger is supplied by Campbell Scientific CR10X-1M data logger with 1 megabyte memory storage module and PC208W Windows logging software. The enclosure and tower is a Campbell Scientific ENC 12\/14 (12\u201d X 14\u201d weather proof enclosure), CM10 (10\u2019 tripod, grounding k kit. Data telemetry\/Communication at Remote Site\/Weather station, Governor’s Academy – RF 310M, RF Modem for use w\/Maxon radios – RF 310, Maxon radio for 148-174 MHZ \u2013 158.34 MHZ – Yagi antenna \u2013 Antennex Y1503 3DB 150-174 MHz at Base station, Rowley field station – RF 310B Base station for use w\/Maxon radio – FG1563, Omni directional antenna 155-162 MHZ, 3 DB gain Sensors used: Temperature and Humidity Vaisala HMP45C temperature and humidity sensor with solar radiation shield. Specifications: +\/- 1% accuracy over Precipitation gauge Texas Electronic TE525WS-L, 8\u201d rain gage with CS705 precipitation adapter for snow fall. Specifications: +\/- 1% accuracy up to Pyranometer Solar radiation, 400-1100 nm, Licor LI200X-L pyranometer, mounted at 3 meter height Specifications: +\/- 3% typical accuracy Solar radiation, 400-1100 nm, Licor LI200X-L pyranometer, mounted at 3 meter height Specifications: +\/- 3% typical accuracy PAR Photosynthetically active radiation, 400-700 nm, Licor LI190SB quantum sensor, mounted at 3 meter height Specifications: +\/- 5% calibration, National Institute of Standards Technology Wind speed and direction RM Young 05103 Wind monitor, mounted at 3 meter height Barometric pressure Vaisala PTB110 Specifications: +\/- 1 mB @ -20oC to +45o C. April 16, 2019 Precipitation snow adapter removed May 22, 2019 Temp\/RH HMP45C radiation shield cleaned of debris lodged between panels, PTB101B barometer sensor repalced with December 6, 2019 Precipitation snow adapter put on for Winter season Sampling and\/or Lab Protocols Protocol Title URL of online Protocol OR Protocol Document VARIABLE DESCRIPTIONS: Variable Name Variable Description Units Date Time date (YYYY-MM-DD) time of day (hh:mm), (eastern standard time EST), 24 hour format Julian julian day number Precip total precipitation, wet only, rain and snow (mm) millimeter Pyranometer solar radiation (kW m-2), 15 minute average kilowattPerMeterSquared -2 -1 PAR photosynthetically active radiation (\u00b5M m sec ), 15 minute average micromolePerMeterSquaredPerSecond Temp air temperature (Celsius), 15 minute average celsius RH relative humidity (percent), 15 minute average percent Wind wind speed (m sec-1), 15 minute average meterPerSecond WindDir wind direction (degrees), 15 minute average degree BAR barometer (mbar), 15 minute average millibar Comments Comments, notes about data Investigator 3 Site 3 ns; Carex aquatilis var. aquatilis; Carex atlantica ssp. atlantica ecipitation, solar radiation, photosynthetically urements every 5 secs and measurements are and direction and barometric pressure. Marshview Farm in Newbury, MA. charger and regulator and 12 AH lead acid storage module and PC208W Windows data M10 (10\u2019 tripod, grounding k it and CM10 guy wire % accuracy over \u2013 40 to 60 \u00b0C temperature range tions: +\/- 1% accuracy up to 1 in.\/hr. ter height 1B barometer sensor repalced with PTB110 datetime Missing DateTim Value e Format Code YYYYMM-DD datetime hh:mm Measurement Scale Code Number Informati Type on interval integer NA ratio real NA ratio real NA ratio real NA interval real NA ratio real NA ratio real NA interval real NA ratio real NA nominal NA Missing Value Code Explanation Do Not Modify. This is the lists for the drop MeasurementScale Number Type Unit Name NA = not available NA = not available NA = not available NA = not available NA = not available NA = not available NA = not available NA = not available NA = not available NA = not available datetime integer ampere interval natural amperePerMeter nominal real amperePerSquareMeter ordinal whole angstrom ratio atmosphere bar calorie celsius centigram centimeter centimeterPerYear centimetersPerSecond centimolesChargePerKi centisecond coulomb cubicCentimetersPerCu cubicFeetPerSecond cubicMeter cubicMeterPerKilogram cubicMetersPerSecond cubicMicrometersPerGr decibar decigram decimeter decisecond degree dimensionless disintegrationsPerMinute farad gram gramsPer0.04SquareMe gramsPerCentimeterSqu gramsPerCubicCentime gramsPerGram gramsPerHectarePerDa gramsPerLiter gramsPerLiterPerDay gramsPerMeterSquared gramsPerMeterSquared gramsPerMilliliter gramsPerNumber gramsPerSquareMeter gramsPerYear gray hectare hectoPascal henry hertz hour joule joulesPerCentimeterSqu joulesPerCentimeterSqu kelvin kilogram kilogramPerCubicMeter kilogramsPerHectare kilogramsPerHectarePe kilogramsPerMeterSqua kilogramsPerMeterSqua kilogramsPerSecond kilogramsPerSquareMet kilohertz kiloliter kilometer kilometersPerHour kilopascal kilosecond kilovolt kilowatt kilowattPerMeterSquare liter litersPerHectare litersPerSecond litersPerSquareMeter lumen lux megagram megahertz megameter megapascal megasecond megavolt megawatt meter metersPerDay metersPerGram metersPerSecond metersPerSecondSquar metersSquaredPerDay metersSquaredPerSeco microCuriePerMicroMole microEinsteinsPerSquar microEinsteinsPerSquar microequivalentsPerLite microgram microgramsPerCubicCe microgramsPerGram microgramsPerLiter microgramsPerMilliliter microliter micrometer microMolesCarbonPerM microMolesPerKilogram microMolesPerLiter microMolesPerSquareM microMolesPerSquareM micron microsecond microsiemensPerCentim millibar milligram milliGramPerKilogram milliGramPerSegment milligramsPerCubicMete milligramsPerLiter milliGramsPerMilliLiter milligramsPerMillimeter milligramsPerSquareMe milligramsPerSquareMe millihertz milliliter milliliterPerLiter millimeter millimetersPerNumber millimetersPerSecond millimetersPerSegment millimolesPerGram millimolesPerMole millimolesPerSquareMe millisecond millivolt milliwatt minute molality molarity mole molePerCubicMeter molesPerGram molesPerKilogram molesPerKilogramPerSe molesPerMeterSqaureP nanogram nanometer nanomolesPerGramPer nanosecond newton nominalDay nominalYear number numberPerGram numberPerKilometerSqu numberPerMeterCubed numberPerMeterSquare numberPerMilliliter numberPerSquareCenti ohm ohmMeter partsPerMillion partsPerThousand pascal percent picoMolesPerLiter picoMolesPerLiterPerHo PSU radian second serialDateNumberYear0 siemen siemensPerMeter squareCentimeters squareCentimetersPer0 squareCentimetersPerG squareKilometers squareMeter squareMeterPerKilogram squareMeterPerNumber squareMeterPerSquareM squareMillimeters volt watt s the lists for the drop-downs. ubicCentimetersPerCubicCentimeters ramsPerCentimeterSquaredPerSecond ramsPerMeterSquaredPerYear oulesPerCentimeterSquaredPerDay oulesPerCentimeterSquaredPerHour logramsPerMeterSquaredPerSecond logramsPerMeterSquaredPerYear microEinsteinsPerSquareMeterPerSecond microMolesCarbonPerMicroMolePhoton microMolesPerSquareMeterPerMinute microMolesPerSquareMeterPerSecond milligramsPerSquareMeterPerDay millimolesPerSquareMeterPerHour umberPerSquareCentimeterPerHour quareCentimetersPer0.04SquareMeter Date Time Julian Precip Pyranometer PAR Temp RH Wind WindDir BAR 2019-01-01 00:15 1 1.016 0.000 0.174 2.52 102 2.06 141.3 1012 2019-01-01 00:30 1 1.016 0.000 0.806 3.12 102 2.52 146.2 1012 2019-01-01 00:45 1 1.270 0.000 0.523 3.63 102 3.20 142.7 1012 2019-01-01 01:00 1 1.016 0.000 0.834 3.99 101 2.49 139.4 1011 2019-01-01 01:15 1 1.524 0.000 0.273 4.19 101 2.53 142.2 1010 2019-01-01 01:30 1 1.016 0.000 0.042 4.34 101 1.90 140.3 1010 2019-01-01 01:45 1 0.762 0.000 0.000 4.64 101 2.76 144.1 1009 2019-01-01 02:00 1 0.508 0.000 0.000 4.86 101 2.14 137.8 1008 2019-01-01 02:15 1 0.762 0.000 0.000 5.05 101 2.22 139.7 1007 2019-01-01 02:30 1 0.508 0.000 0.000 5.29 101 2.80 145.1 1005 2019-01-01 02:45 1 0.254 0.000 0.000 5.46 102 2.79 134.9 1004 2019-01-01 03:00 1 0.000 0.000 0.000 5.63 102 2.23 149.4 1003 2019-01-01 03:15 1 0.254 0.000 0.000 5.84 102 2.70 141.6 1003 2019-01-01 03:30 1 0.000 0.000 0.221 6.02 102 2.55 146.1 1002 2019-01-01 03:45 1 0.000 0.000 0.584 6.17 102 1.88 148.4 1001 2019-01-01 04:00 1 0.254 0.000 0.000 6.35 102 2.16 160.1 1001 2019-01-01 04:15 1 0.000 0.000 0.000 6.49 102 2.72 150.2 1000 2019-01-01 04:30 1 0.254 0.000 0.000 6.53 102 1.40 164.2 999 2019-01-01 04:45 1 0.254 0.000 0.000 6.55 102 0.88 204.5 999 2019-01-01 05:00 1 0.254 0.000 0.000 6.53 102 0.65 183.8 998 2019-01-01 05:15 1 0.000 0.000 0.000 6.48 102 0.69 206.7 997 2019-01-01 05:30 1 0.000 0.000 0.000 6.44 102 0.64 241.5 997 2019-01-01 05:45 1 0.254 0.000 0.000 6.34 102 0.52 232.7 996 2019-01-01 06:00 1 0.000 0.000 0.000 6.29 103 0.92 241.5 996 2019-01-01 06:15 1 0.000 0.000 0.042 6.27 103 1.06 230.6 996 2019-01-01 06:30 1 0.000 0.000 0.085 6.41 103 1.16 257.5 996 2019-01-01 06:45 1 0.000 0.000 0.019 6.44 103 1.18 257.6 996 2019-01-01 07:00 1 0.000 0.000 0.118 6.34 103 0.78 241.5 996 2019-01-01 07:15 1 0.000 0.001 1.728 5.97 103 1.44 233.6 996 2019-01-01 07:30 1 0.000 0.004 8.980 5.58 103 1.21 245.5 996 2019-01-01 07:45 1 0.000 0.011 25.620 5.16 103 1.49 230.3 996 2019-01-01 08:00 1 0.000 0.013 35.890 4.93 103 1.64 255.3 996 2019-01-01 08:15 1 0.000 0.017 56.350 4.76 103 1.31 247.3 996 2019-01-01 08:30 1 0.000 0.208 357.400 5.11 103 1.82 248.6 996 2019-01-01 08:45 1 0.000 0.060 114.800 5.71 103 1.52 245.5 996 2019-01-01 09:00 1 0.000 0.030 72.500 6.05 103 0.81 241.7 996 2019-01-01 09:15 1 0.000 0.100 219.800 6.75 103 1.17 253.0 996 2019-01-01 09:30 1 0.000 0.201 400.500 7.71 103 1.02 260.5 997 2019-01-01 09:45 1 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A Sample Answer For the Assignment: BIOL 339 CUNY Borough of Manhattan Community College Data Manipulation Worksheet<\/strong><\/h2>\n

Title: BIOL 339 CUNY Borough of Manhattan Community College Data Manipulation Worksheet<\/strong><\/h2>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

BIOL 339 CUNY Borough of Manhattan Community College Data Manipulation Worksheet Sample Answer for BIOL 339 CUNY Borough of Manhattan Community College Data Manipulation Worksheet Included After Question Description has two scientific goals: 1) to learn how to summarize data and 2) to help you gain a better sense of the terrestrial weather variables that […]<\/p>\n","protected":false},"author":11,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-29563","post","type-post","status-publish","format-standard","hentry","category-nursing-papers"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":6}},"yoast_head":"\nBIOL 339 CUNY Borough of Manhattan Community College Data Manipulation Worksheet - Nursing Assignment Crackers<\/title>\n<meta name=\"description\" content=\"BIOL 339 CUNY Borough of 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